






"""simple docstring"""


import argparse
import os
from pathlib import Path
from typing import Dict

import tensorflow as tf
import torch
from tqdm import tqdm

from transformers import PegasusConfig, PegasusForConditionalGeneration, PegasusTokenizer
from transformers.models.pegasus.configuration_pegasus import DEFAULTS, task_specific_params


__SCREAMING_SNAKE_CASE   =   [
    # replace left string with right string to get the relevant state_dict key (identical state dict to bart)
    ['memory_attention', 'encoder_attn'],
    ['attention', 'attn'],
    ['/', '.'],
    ['.LayerNorm.gamma', '_layer_norm.weight'],
    ['.LayerNorm.beta', '_layer_norm.bias'],
    ['r.layer_', 'r.layers.'],
    ['output_proj', 'out_proj'],
    ['ffn.dense_1.', 'fc2.'],
    ['ffn.dense.', 'fc1.'],
    ['ffn_layer_norm', 'final_layer_norm'],
    ['kernel', 'weight'],
    ['encoder_layer_norm.', 'encoder.layer_norm.'],
    ['decoder_layer_norm.', 'decoder.layer_norm.'],
    ['embeddings.weights', 'shared.weight'],
]


def        UpperCAmelCase      (					a__ ):





						'''simple docstring'''






						for pegasus_name, hf_name in PATTERNS:
												lowerCAmelCase   :Any						=			k.replace(a__     ,      a__ )
						return k


def        UpperCAmelCase      (					a__     ,      a__ ):





						'''simple docstring'''






						lowerCAmelCase   :Tuple						=			DEFAULTS.copy()
						cfg_kwargs.update(a__ )
						lowerCAmelCase   :List[str]						=			PegasusConfig(**a__ )
						lowerCAmelCase   :Union[str, Any]						=			PegasusForConditionalGeneration(a__ )
						lowerCAmelCase   :Union[str, Any]						=			torch_model.model.state_dict()
						lowerCAmelCase   :List[Any]						=			{}
						for k, v in tf_weights.items():
												lowerCAmelCase   :List[str]						=			rename_state_dict_key(a__ )
												if new_k not in sd:
																		raise ValueError(F"""could not find new key {new_k} in state dict. (converted from {k})""" )

												if "dense" in k or "proj" in new_k:
																		lowerCAmelCase   :Tuple						=			v.T
												lowerCAmelCase   :Optional[int]						=			torch.tensor(a__     ,      dtype=sd[new_k].dtype )
												assert v.shape == sd[new_k].shape, F"""{new_k}, {k}, {v.shape}, {sd[new_k].shape}"""
						# make sure embedding.padding_idx is respected
						lowerCAmelCase   :Optional[int]						=			torch.zeros_like(mapping['shared.weight'][cfg.pad_token_id + 1] )
						lowerCAmelCase   :Union[str, Any]						=			mapping['shared.weight']
						lowerCAmelCase   :List[Any]						=			mapping['shared.weight']
						lowerCAmelCase   :Optional[Any]						=			{k: torch.zeros_like(a__ ) for k, v in sd.items() if k.endswith('bias' ) and k not in mapping}
						mapping.update(**a__ )
						lowerCAmelCase	, lowerCAmelCase   :List[Any]						=			torch_model.model.load_state_dict(a__     ,      strict=a__ )
						lowerCAmelCase   :str						=			[
						    k for k in missing if k not in ['encoder.embed_positions.weight', 'decoder.embed_positions.weight']
						]
						assert unexpected_missing == [], F"""no matches found for the following torch keys {unexpected_missing}"""
						assert extra == [], F"""no matches found for the following tf keys {extra}"""
						return torch_model


def        UpperCAmelCase      (					a__="./ckpt/aeslc/model.ckpt-32000" ):





						'''simple docstring'''






						lowerCAmelCase   :Tuple						=			tf.train.list_variables(a__ )
						lowerCAmelCase   :Dict						=			{}
						lowerCAmelCase   :Optional[int]						=			['Adafactor', 'global_step']
						for name, shape in tqdm(a__     ,      desc='converting tf checkpoint to dict' ):
												lowerCAmelCase   :Tuple						=			any(pat in name for pat in ignore_name )
												if skip_key:
																		continue
												lowerCAmelCase   :List[str]						=			tf.train.load_variable(a__     ,      a__ )
												lowerCAmelCase   :int						=			array
						return tf_weights


def        UpperCAmelCase      (					a__     ,      a__ ):





						'''simple docstring'''






						lowerCAmelCase   :Any						=			Path(a__ ).parent.name
						lowerCAmelCase   :List[Any]						=			task_specific_params[F"""summarization_{dataset}"""]['max_position_embeddings']
						lowerCAmelCase   :Optional[int]						=			PegasusTokenizer.from_pretrained('sshleifer/pegasus'     ,      model_max_length=a__ )
						assert tok.model_max_length == desired_max_model_length
						tok.save_pretrained(a__ )

						# convert model
						lowerCAmelCase   :Optional[Any]						=			get_tf_weights_as_numpy(a__ )
						lowerCAmelCase   :Any						=			task_specific_params[F"""summarization_{dataset}"""]
						if dataset == "large":
												lowerCAmelCase   :int						=			task_specific_params
						lowerCAmelCase   :Union[str, Any]						=			convert_pegasus(a__     ,      a__ )
						torch_model.save_pretrained(a__ )
						lowerCAmelCase   :Optional[int]						=			torch_model.state_dict()
						sd.pop('model.decoder.embed_positions.weight' )
						sd.pop('model.encoder.embed_positions.weight' )
						torch.save(a__     ,      Path(a__ ) / 'pytorch_model.bin' )


if __name__ == "__main__":
		__SCREAMING_SNAKE_CASE   =   argparse.ArgumentParser()
		# Required parameters
		parser.add_argument('tf_ckpt_path', type=str, help='passed to tf.train.list_variables')
		parser.add_argument('save_dir', default=None, type=str, help='Path to the output PyTorch model.')
		__SCREAMING_SNAKE_CASE   =   parser.parse_args()
		if args.save_dir is None:
				__SCREAMING_SNAKE_CASE   =   Path(args.tf_ckpt_path).parent.name
				__SCREAMING_SNAKE_CASE   =   os.path.join('pegasus', dataset)
		convert_pegasus_ckpt_to_pytorch(args.tf_ckpt_path, args.save_dir)







"""simple docstring"""


import unittest

import numpy as np

from diffusers import OnnxStableDiffusionInpaintPipelineLegacy
from diffusers.utils.testing_utils import (
    is_onnx_available,
    load_image,
    load_numpy,
    nightly,
    require_onnxruntime,
    require_torch_gpu,
)


if is_onnx_available():
		import onnxruntime as ort




@nightly
@require_onnxruntime
@require_torch_gpu
class 						__UpperCamelCase     (     unittest.TestCase						):
					@property
					def        UpperCAmelCase__ (							self							:	Union[str, Any]   )	->       Dict:
											return (
											    "CUDAExecutionProvider",
											    {
											        "gpu_mem_limit": "15000000000",  # 15GB
											        "arena_extend_strategy": "kSameAsRequested",
											    },
											)
					@property
					def        UpperCAmelCase__ (							self							:	int   )	->       Optional[Any]:
											lowerCAmelCase   :Any						=			ort.SessionOptions()
											lowerCAmelCase   :Dict						=			False
											return options
					def        UpperCAmelCase__ (							self							:	Tuple   )	->       str:
											lowerCAmelCase   :Union[str, Any]						=			load_image(
											    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
											    '/in_paint/overture-creations-5sI6fQgYIuo.png'   )
											lowerCAmelCase   :str						=			load_image(
											    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
											    '/in_paint/overture-creations-5sI6fQgYIuo_mask.png'   )
											lowerCAmelCase   :List[Any]						=			load_numpy(
											    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
											    '/in_paint/red_cat_sitting_on_a_park_bench_onnx.npy'   )

											# using the PNDM scheduler by default
											lowerCAmelCase   :Tuple						=			OnnxStableDiffusionInpaintPipelineLegacy.from_pretrained(
											    'CompVis/stable-diffusion-v1-4'     , revision='onnx'     , safety_checker=UpperCAmelCase     , feature_extractor=UpperCAmelCase     , provider=self.gpu_provider     , sess_options=self.gpu_options     , )
											pipe.set_progress_bar_config(disable=UpperCAmelCase   )

											lowerCAmelCase   :Optional[Any]						=			'A red cat sitting on a park bench'

											lowerCAmelCase   :Optional[Any]						=			np.random.RandomState(0   )
											lowerCAmelCase   :Union[str, Any]						=			pipe(
											    prompt=UpperCAmelCase     , image=UpperCAmelCase     , mask_image=UpperCAmelCase     , strength=0.7_5     , guidance_scale=7.5     , num_inference_steps=15     , generator=UpperCAmelCase     , output_type='np'     , )

											lowerCAmelCase   :Optional[int]						=			output.images[0]

											assert image.shape == (512, 512, 3)
											assert np.abs(expected_image - image   ).max() < 1e-2